Assignment 04: Data Visualization — ggplot2 and Beyond

Author

Affiliation

Alona Nazarenko

Kyiv School of Economics

Data on Vehicles for 2025 🚗

As I’ve already visualized my previous dataset about freedom, I was looking for something new and interesting. During a lecture, Ihor Miroshnychenko showed us a website with open data for New York 🌆. That got me curious, so I searched the web and found a similar site for Ukraine 🇺🇦.

I easily found a dataset covering vehicles from 01.01.2025 up to the most recent data 30.09.2025. You can check it out on the DIA portal 📊.

This dataset is very large, containing 1,641,470 rows and 22 columns:

car_data <- read_csv2("reestrtz30.09.2025.csv")
car_data

# A tibble: 1,641,470 × 20
   PERSON REG_ADDR_KOATUU OPER_CODE OPER_NAME   D_REG DEP_CODE DEP   BRAND MODEL
   <chr>  <chr>               <dbl> <chr>       <chr>    <dbl> <chr> <chr> <chr>
 1 P      6310138200            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… LEXUS IS 2…
 2 P      3222486201            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… MG    350  
 3 P      3521485801            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… SKODA SUPE…
 4 P      8000000000            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… TOYO… RAV-…
 5 P      5120410100            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… RENA… KANG…
 6 P      8036300000            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… CHEV… ORLA…
 7 P      4623683701            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… NISS… LEAF 
 8 P      8039100000            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… RENA… MEGA…
 9 P      8038500000            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… TOYO… PROA…
10 P      1210100000            254 НАЛЕЖНИЙ К… 01.0…    10000 OLD_… VOLK… TIGU…
# ℹ 1,641,460 more rows
# ℹ 11 more variables: VIN <chr>, MAKE_YEAR <dbl>, COLOR <chr>, KIND <chr>,
#   BODY <chr>, PURPOSE <chr>, FUEL <chr>, CAPACITY <dbl>, OWN_WEIGHT <dbl>,
#   TOTAL_WEIGHT <dbl>, N_REG_NEW <chr>

Column Name	Description
person	Type of vehicle owner: P = private person, J = legal entity
reg_addr_koatuu	KOATUU code of the registration address (administrative territorial code).
oper_code	Operation code indicating type of registration or action.
oper_name	Description of the operation (e.g., “НАЛЕЖНИЙ КОРИСТУВАЧ…”).
d_reg	Registration date of the vehicle.
dep_code	Code of the department or registration office.
dep	Name of the department or office.
brand	Vehicle brand (e.g., TOYOTA, BMW).
model	Vehicle model.
vin	Vehicle Identification Number (unique identifier for the vehicle).
make_year	Year the vehicle was manufactured.
color	Color of the vehicle.
kind	Vehicle type (e.g., car, truck, motorcycle).
body	Body type (e.g., sedan, SUV, hatchback).
purpose	Vehicle purpose (e.g., personal, commercial).
fuel	Fuel type (e.g., petrol, diesel, electric).
capacity	Engine capacity in cubic centimeters (cc).
own_weight	Vehicle’s own weight (kg).
total_weight	Vehicle’s total weight including load (kg).
n_reg_new	Indicator of new registration (yes/no or code).

Data Preparation

Here, I make the dataset easier to work with by categorizing the operations and identifying the region codes where each operation was performed.

car_data <- car_data |> 
  clean_names() |> 
  mutate(d_reg = dmy(d_reg),
         category_oper = case_when(
           str_detect(oper_name, "ПЕРВИННА|(^|\\s)РЕЄСТРАЦІЯ") ~ "Registration",
           str_detect(oper_name, "ПЕРЕРЕЄСТРАЦІЯ") ~ "Re_registration",
           str_detect(oper_name, "ТИМЧАС") ~ "Temporary",
           str_detect(oper_name, "ЗНЯТТЯ З ОБЛІКУ|СКАСУВАННЯ ЗНЯТТЯ") ~ "Deregistration",
           TRUE ~ "Other"
         ),
         number_obl = str_extract(n_reg_new, "^[А-ЯІA-Z]{2}"))

Web Scraping 🕸️

For my analysis, having only the region code wasn’t enough — I also needed the region name.
So, I used web scraping to create a table that decodes the region codes from this site.

num_site <- read_html("https://avtonomera.net.ua/ua/korysna-informatsiya/kody-avtomobilnykh-nomeriv-diznatysya-kod-rehionu-za-nomerom-mashyny-nomery-rehioniv-ukrayiny.html?srsltid=AfmBOopXkW32murezYkxdKyhBHcr0kUIp52nKuczEcgmX4sDybAloNkm")

number_df <- num_site |> 
  html_nodes("table") |> 
  html_table(fill = TRUE)

number_df <- number_df[[1]] |>
  clean_names() |>
  slice(-1) |>
  select(c(region_ukraini, z_2004_roku_liternij_kod_zliva)) |>
  set_names(c("region_ukraini", "code")) |> 
  separate_rows(code, sep = ",\\s*") |>
  mutate(
    code = str_trim(code),
    code = str_replace_all(code, "I", "І")
  ) 

And also a big pain was the letter “І”, because it looks almost identical to the Latin “I” in some fonts, which caused issues when matching or merging data. I had to carefully normalize all the text to make sure the Ukrainian letters were correctly recognized.

Insights & Graphs 📊

The first thing I was curious about was which car brands Ukrainians prefer the most. To my surprise, it is Volkswagen.

car_data |> 
  filter(category_oper %in% c("Registration", "Re_registration") & kind == "ЛЕГКОВИЙ") |> 
  count(brand) |> 
  slice_max(n, n = 10) |> 
  mutate(
    logo = file.path("logos", paste0(brand, ".png")),
         brand = fct_reorder(brand, n)
    ) |> 
  ggplot(aes(brand, n, fill = brand)) + 
  geom_col() +
  geom_image(aes(y = n + max(n) * 0.05, image = logo), size = 0.1) +
  geom_text(aes(y = n - max(n) * 0.18,
                label = scales::comma(n),
                color = brand), 
            hjust = 0, size = 4)+
  scale_fill_manual(
                     values = c(
    "#C4DDFF",  
    "#A0C4FF",
    "#7DA9FF",
    "#5C81F2",
    "#4B6ECF",
    "#3C5AA6",
    "#2E4A7D",
    "#23395B",
    "#1B263B",
    "#0D1B2A"   
  )) + 
  scale_color_manual(
    values = c(
      "#0D1B2A", 
      "#0D1B2A", 
      "#0D1B2A", 
      "#0D1B2A", 
      "#0D1B2A",
      "#C4DDFF", 
      "#C4DDFF", 
      "#C4DDFF", 
      "#C4DDFF", 
      "#C4DDFF"
    )) +
  scale_y_continuous(labels = comma) +
  coord_flip() +
  labs(
    title = "Top 10 Car Brands by Registrations",
    x = "Brand",
    y = "Number of Registrations"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.y = element_text(face = "bold", hjust = 1),
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.position = "none"
  )

I also did a deeper analysis and found that the most popular car model is the Volkswagen Passat 2012, with a price range of about $9,000–$10,000. This may be influenced by the average Ukrainian salary and general affordability, which makes cars like the Volkswagen Passat 2012 more accessible.

Volkswagen Passat 2012

Next Prey 🐾

My next prey was the distribution of car types prowling our roads.

year_graph <- car_data |> 
  filter((category_oper == "Registration" | 
            category_oper == "Re_registration")) |>
  mutate(kind = fct_reorder(kind, make_year, .fun = median)) |>
  ggplot(aes(x = reorder(kind, make_year, median), y = make_year, colour = kind)) + 
  geom_boxplot(outlier.alpha = 0.5) +
  scale_y_continuous(
    breaks = seq(min(car_data$make_year), max(car_data$make_year), by = 10)
  )+
  scale_colour_manual(values = c(
    "#000000",  
    "#1F5B89",  
    "#337AB7",
    "#F25C05",  
    "#FF9F1C",  
    "#004D4D",  
    "#009999",  
    "#FF4FA3",  
    "#C71585" ) 
    )+
  labs(
    title = "Distribution of Vehicle Production Years by Type in Ukraine🇺🇦",
    x = "Type of Vehicle",
    y = "Year of Manufacture"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.position = "none" 
  )

ggplotly(year_graph)

Most cars are up to 20 years old 🚗. The preference for older vehicles likely reflects affordability 💰, highlighting the importance of increasing income levels 📊.

A few unusual outliers caught my eye—cars over 1000 years old 🕰️🚘—which I couldn’t resist exploring 🔎.

Table 1: Outliers

brand	model	make_year	color	purpose	fuel	vin	number_obl
ЗИЛ	130	1900	ЗЕЛЕНИЙ	СПЕЦІАЛЬНИЙ	БЕНЗИН АБО ГАЗ	00000000000000146	NA
ЗИЛ	130	1900	ЗЕЛЕНИЙ	СПЕЦІАЛЬНИЙ	БЕНЗИН	1021382	NA
HONDA	LEAD	1900	СІРИЙ	ЗАГАЛЬНИЙ	БЕНЗИН	JH1000AF481009931	ВА
ГАЗ	3307	1900	СИНІЙ	ЗАГАЛЬНИЙ	БЕНЗИН	XTH330700P1452790	NA
АЗЛК	2140	1900	СИНІЙ	ЗАГАЛЬНИЙ	БЕНЗИН	NA	NA
PACKARD	180	1900	ЧОРНИЙ	ЗАГАЛЬНИЙ	БЕНЗИН	128213520	СЕ
SUZUKI	LETS	1900	ЧЕРВОНИЙ	ЗАГАЛЬНИЙ	БЕНЗИН	JS1000CA1KA129840	NA
PACKARD	180	1900	ЧОРНИЙ	ЗАГАЛЬНИЙ	БЕНЗИН	128213520	СЕ

Packard 180

From the next graph, we can clearly see that in 2025, most people are expected to register cars manufactured in 2008 🚗 — the year that stands out as the peak of registrations.

car_data |> 
  filter((category_oper == "Registration" | 
            category_oper == "Re_registration")) |> 
  select(make_year) |> 
  ggplot(aes(make_year)) + 
  geom_density(fill = "#009999", alpha = 0.5) +  
  labs(
    title = "Distribution of Vehicle Make Year",
    x = "Make Year",
    y = "Density"
  ) +
  scale_x_continuous(
    limits = c(1980, max(car_data$make_year)),     
    breaks = seq(1980, max(car_data$make_year), 5) 
  ) +
  theme_minimal(base_size = 14) +
  geom_text(
    data = peak,
    aes(make_year, density_year, label = (round(make_year))),
    vjust = - 1.8,
    color = "#004D4D",
    check_overlap = TRUE, 
    fontface = "bold",
    size = 4.5
  )

Warning: Removed 3276 rows containing non-finite outside the scale range
(`stat_density()`).

🌱 Ecology

I also decided to explore how eco-friendly our country is this year 🌍.
Below is a map of Ukraine 🇺🇦 showing the number of electric ⚡ cars in each region.

electro_graph <- ggplot(ukraine_sf) +
  geom_sf(aes(
    fill = electro,
    text = glue("Region: {NAME_1}
    Electric: {electro}
    Total registrations: {total}
    Share: {round(electro / total * 100, 1)}%")
    ), color = "black") +
  scale_fill_gradientn(
    colours = c("#F7FEE7", "#A3E635", "#65A30D", "#3F6212", "#1A2E05"),
    na.value = "grey90",
    name = "Electric Cars"
  ) + theme_minimal(base_size = 14) +
  labs(title = glue("\U000026A1 Electric Mobility Across Ukraine")) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", color = "#14532D"),
    plot.caption = element_text(hjust = 0.5, face = "italic", color = "#14532D"),
    panel.background = element_rect(fill = "#F8FFF8", color = NA)
  )

Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
: Ignoring unknown aesthetics: text

ggplotly(electro_graph, tooltip = "text") |> 
  layout(
    hoverlabel = list(
      bgcolor = "#F7FEE7",   
      bordercolor = "#65A30D",
      font = list(color = "#3F6212", size = 13)
    )
  )

Registrations via DIA portal: 1589
Total electric car registrations: 91661

Here we can see the top leaders in electric car adoption ⚡:

• 🏙️ Kyiv
  
• 🏞️ Lviv
  
• 🌊 Dnipro

It is sad to say 💔, but Kharkiv no longer has the same capacity as before, and all frontline regions ⚠️ are similarly affected.

In contrast, the western regions 🌄 are making much greater progress in adopting electric vehicles 🚗🔋.
This is yet another consequence of the war ⚔️.